The MNTN Performance ML team helps brands reach the right customers with software that turns petabytes of data into meaningful campaign strategies. Our engineers, data scientists and analysts build software that serves content to millions of people every day. As a Senior Machine Learning Engineer, you will focus on operationalizing machine learning models by taking ownership of prototypes built by data scientists and turning them into robust, scalable production systems. You will lead the deployment, monitoring, and maintenance of ML solutions that power campaign optimizations at scale. This role emphasizes strong software engineering practices, designing for reliability and performance, and working with large-scale data pipelines and infrastructure. You’ll collaborate across functions to ensure models are not just accurate but production-ready, scalable, and cost-effective. This is a senior machine learning role with an emphasis on building production-ready models. It is not a pure research role. You are expected to ship production-grade implementations and own outcomes in production.

What You’ll Do:

• Design and build a robust marketing platform that reaches the right audience, anywhere, anytime
• Build high volume services that are reliable at scale
• Develop big data solutions using open source frameworks
• Collaborate with and explain complex technical issues to Product and Project Leads
• Joint production ownership with shared on-call participation
• Focus on:
  • improving service reliability, latency, and observability
  • improving model quality (including false positives, thresholds, calibration)
  • speeding up model testing loops
  • releasing model and service changes faster, with confidence
  • improving data/pipeline freshness and delivery velocityDesign, train, evaluate, and improve models for deliverability, forecasting, and optimization.

• Improve thresholding, calibration, and guardrail logic to reduce false positives and decision noise.
• Build robust offline/online evaluation workflows tied to business outcomes.
• Work directly in production codepaths to ship model improvements safely.
• Partner closely with platform-leaning MLEs on reliability, rollout safety, and observability.
• Share on-call responsibility for production ML services.

What Success Looks Like:

• Model quality improves on agreed business and operational metrics.
• False positives and unstable decision behavior are reduced in key flows.
• Model testing/evaluation cycles become materially faster.
• More model improvements reach production safely and predictably.

What You’ll Bring:

• 5+ years building ML models that were deployed and operated in production.
• Strong applied ML fundamentals (classification/regression/forecasting + evaluation rigor).
• Strong Python and SQL with production engineering discipline (testing, maintainability, performance).
• Experience balancing model quality, system constraints, and speed-to-production.
• Strong experience with ownership and cross-functional collaboration.
• Experience in ad tech, growth analytics, personalization, or performance marketing
• Proficiency working with real-time or near-real-time data pipelines
• Experience with experimentation frameworks and production model monitoring.
• Experience with Kedro, AutoGluon, PyTorch, Polars, BigQuery/GCP, and Airflow/SQLMesh ecosystems.